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Microplastics (MPs), typically defined as plastic fragments smaller than 5 mm, are pervasive in terrestrial and marine ecosystems. There is a need for rapid, portable, low-cost detection systems to assess health and environmental risks. Fluorescent tagging with Nile Red (NR) has emerged as a popular detection method, but variations in fluorescent emissions based on NR solvent, plastic polymer, excitation wavelength, and additives complicate standardization. In this study, seven plastic samples stained with acetone-based NR were analyzed using a fluorescent spectrometer to identify optimal emission peaks across UV-Vis excitation wavelengths. These findings aid in selecting appropriate excitation wavelengths and optical filters for future detection systems. Additionally, a straightforward polymer identification scheme was validated against field-collected plastic samples, whose material composition was confirmed via Fourier Transform Infrared Spectroscopy. This work contributes towards developing accessible microplastic detection technologies by characterizing the fluorescent properties of NR-stained plastics and enhancing the capability for effective environmental monitoring. Future research will expand the dataset to include diverse plastics with varying additives and weathering, and incorporate computer-vision tools for automated data processing and polymer identification.

Slender marine structures such as deep-water riser systems are continuously exposed to currents, leading to vortex-induced vibrations (VIV) of the structure. This may result in amplified drag loads and fast accumulation of fatigue damage. Consequently, accurate prediction of VIV responses is of great importance for the safe design and operation of marine risers. Model tests with elastic pipes have shown that VIV responses are influenced by many structural and hydrodynamic parameters, which have not been fully modelled in present frequency domain VIV prediction tools. Traditionally, predictions have been computed using a single set of hydrodynamic parameters, often leading to inconsistent prediction accuracy when compared with observed field measurements and experimental data. Hence, it is necessary to implement a high safety factor of 10–20 in the riser design, which increases development costs and adds extra constraints in the field operation. One way to compensate for the simplifications in the mathematical prediction model is to apply adaptive parameters to describe different riser responses. The objective of this work is to demonstrate a new method to improve the prediction consistency and accuracy by applying adaptive hydrodynamic parameters. In the present work, a four-step approach has been proposed: First, the measured VIV response will be analysed to identify key parameters to represent the response characteristics. These parameters will be grouped by using data clustering algorithms. Secondly, optimal hydrodynamic parameters will be identified for each data group by optimisation against measured data. Thirdly, the VIV response using the obtained parameters will be calculated and the prediction accuracy evaluated. Last but not least, classification algorithms will be applied to determine the correct hydrodynamic parameters to be used for new cases. An iteration of the previous steps may be needed if the prediction accuracy of the new case is not satisfactory. This concept has been demonstrated with examples from experimental data.

Neuromast sensors found in fishes detect flows around their body and are known to generate hydrodynamic awareness. This work reports the development of superficial neuromast (SN) and canal neuromast (CN) inspired MEMS flow sensors. The MEMS artificial neuromast sensors mimic the key features of the SN and the CN such as the shape and the mechanical properties of the cupula. In case of both SN and CN MEMS sensors, hydrogels with varying initiator concentrations were used to replicate the Young’s modulus of the biological cupula. The MEMS SN and CN sensors were experimentally characterized in steady-state and oscillatory flows respectively.

This paper reports development of a novel, flexible hydrogel based capacitive pressure sensor. It features a simple design of hydrogel thin film sandwiched between two gold electrodes. The sensor shows linear increase in output with increase in pressure. The microporous network of hydrogel enables high water retention while its flexible and stretchable structure ensures usage over large areas. These factors, combined with its stability in water, make it an ideal candidate for underwater applications. Further, the physical and chemical properties of hydrogels can be tailored to tune the capacitance to specific sensing needs. Flexible arrays of capacitive sensors with hydrogel hold great potential for underwater smart skin applications.